• Location: Bengaluru / Hyderabad / NCR (Hybrid / Remote Flexibility)
• Employment Type: Full-Time
• Department: Core AI & Machine Learning Engineering
• Experience Range: 5–9 Years

About The Company & AI Engineering Culture As a high-growth deep tech and enterprise SaaS enterprise, our platform bridges complex enterprise data with state-of-the-art Generative AI models. We build custom multi-modal AI pipelines, Retrieval-Augmented Generation (RAG) ecosystems, and fine-tuned large language model architectures that serve hundreds of thousands of enterprise users globally. Our AI engineering culture values experimental rigor, prompt engineering best practices, efficient model quantization, and production-grade inference optimization. We do not just build prototypes; we scale heavy transformer models into low-latency, highly available production endpoints. If you are passionate about pushing the boundaries of applied machine learning and GenAI systems, this is your arena.

Position Overview

We are looking for an expert, hands-on Senior Applied Machine Learning Engineer to design, train, fine-tune, and deploy advanced GenAI and deep learning models into production. In this role, you will own the entire lifecycle of our intelligent services—from data curation and distributed model training to low-latency inference serving and continuous evaluation.

You will collaborate closely with data architects, backend platform engineers, and product managers to translate complex enterprise business cases into scalable, intelligent AI features.

Key Responsibilities & Technical Ownership1. Fine-Tuning & Model Architecture

• LLM Customization: Design and execute fine-tuning strategies (LoRA, QLoRA, DPO, RLHF) on open-weights foundation models (Llama, Mistral, DeepSeek) using distributed training frameworks.
• Advanced RAG Engineering: Architect hybrid retrieval pipelines combining dense vector embeddings, sparse keyword indexing, cross-encoder rerankers, and graph-based context augmentation.
• Multi-Modal Capabilities: Implement and optimize vision-language models and text-to-code generation systems tailored to enterprise domain workflows.
• Inference Optimization & Scalability
• Inference Acceleration: Optimize model inference throughput and latency using TensorRT, vLLM, DeepSpeed, and ONNX runtime formats.
• Quantization & Compression: Implement quantization techniques (AWQ, GPTQ, GGUF) to reduce model memory footprints without sacrificing task accuracy.
• Production Deployment: Containerize models and deploy scalable inference microservices across cloud GPU clusters (AWS EC2 p4/p5 instances, NVIDIA Triton Inference Server).
• Evaluation & MLOps Governance
• Evaluation Frameworks: Build automated evaluation pipelines (Ragas, TruLens) to monitor model hallucination rates, semantic drift, and response latency.
• MLOps Pipelines: Manage model registries, experiment tracking (MLflow / Weights & Biases), and automated CI/CD deployment pipelines for machine learning artifacts.

Comprehensive Tech Stack & Technical RequirementsCore Technical Stack

• Languages: Expert proficiency in Python, with working knowledge of C++ for performance-critical extensions.
• ML Frameworks: PyTorch, Hugging Face (Transformers, PEFT, Datasets), LangChain, LlamaIndex.
• Inference & Serving: vLLM, NVIDIA Triton Inference Server, Ollama, TensorRT-LLM.
• Vector Databases & Storage: Qdrant, Milvus, Pinecone, pgvector, Redis.
• Cloud & Infrastructure: AWS (SageMaker, GPU instances), Docker, Kubernetes, Ray, MLflow.

Experience & Educational Qualifications

• Experience: 5 to 9 years of professional software engineering experience, with at least 3+ years dedicated exclusively to applied machine learning, deep learning, or production-grade generative AI engineering.
• Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline from a premier institution (IITs, NITs, IISc, or top global universities).

Competencies & Behavioral Traits

• Scientific Curiosity: A relentless drive to stay abreast of rapidly evolving research papers, open-source model releases, and architectural breakthroughs in AI.
• Pragmatic Engineering: Ability to balance cutting-edge research methodologies with production constraints like latency budgets, cloud costs, and system reliability.

Professional Interview Process

• Initial Screening: Discussion on machine learning fundamentals, transformer architectures, and past GenAI project delivery.
• Machine Learning System Design: Collaborative design exercise scaling an enterprise RAG pipeline or a high-throughput LLM inference gateway.
• Coding & PyTorch Deep-Dive: Live coding assessment focusing on tensor manipulations, custom loss functions, or optimization scripts.
• Culture Fit & Leadership: Interview with engineering leadership focusing on experimental rigor, ownership, and collaboration.

Skills: vector db,milvus,kubernetes,qdrant,aws sagemaker,pytorch,vllm,models,cloud,huggingface,langchain,llamaindex,docker,nvidia triton,transformer